Trajectory plots

# Grab corrected data
corrected <- suppressMessages(
  covidcast::covidcast_signal(
    state_forecaster_signals$data_source[1], 
    state_forecaster_signals$signal[1],
    start_day = ymd(today) - days(x = corrections_lookback), 
    end_day = forecast_date,
    geo_type = "state")) %>% 
  state_corrector()

corrected <- corrected[[1]] 

# setup the plot and join corrections to the truth
pd <- evalcast:::setup_plot_trajectory(
  antelope, geo_type = "state",
  start_day = lubridate::ymd(today) - lubridate::days(x = corrections_lookback),
  end_day = forecast_date,)
  
pd$truth_df <- left_join(
  pd$truth_df, corrected, 
  by = c("geo_value" = "geo_value", "target_end_date" = "time_value")) %>%
  filter(target_end_date >= ymd(today) - days(x = qa_lookback))

g <- ggplot(pd$truth_df, mapping = aes(x = target_end_date))

# build the fan
g <- g + geom_ribbon(
  data = pd$quantiles_df,
  mapping = aes(ymin = lower, ymax = upper, fill = interval)) +
  scale_fill_brewer(palette = "Blues")

# line layer
g <- g +
  geom_line(aes(y = .data$value.y), color = "#3182BD") + # corrected
  geom_line(aes(y = .data$value.x)) + # reported
  geom_line(data = pd$points_df, 
            mapping = aes(y = .data$value),
            color = "orange", size = 1) +
  geom_point(aes(y = .data$value.x)) + # reported gets dots
  geom_point(data = pd$points_df, 
             mapping = aes(y = .data$value),
             color = "orange", size = 3)

g + theme_bw(base_size = 20) + 
  facet_wrap(~geo_value, scales = "free_y", ncol = 5) +
  theme(legend.position = "none") + ylab("") + xlab("")

Compare to hub

## Fetched day 2021-12-01 to 2022-01-13: num_entries = 2376